{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6b492e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "高中体测数据可视化（体测分数_男生，体测分数-女生）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d86188ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35b2249e",
   "metadata": {},
   "outputs": [],
   "source": [
    "1、对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1524e16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0        0  2785      62  170  72.599998  25.120001  \n",
       "1       60  3133      68  174  52.700001  17.410000  \n",
       "2        0  3901      80  169  46.500000  16.280001  \n",
       "3        0  4946     100  183  79.699997  23.799999  \n",
       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
       "474     85  5755     100  181  65.000000  19.840000  \n",
       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy=pd.read_excel('./体测分数_男生.xls')\n",
    "boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03285537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男生1000米跑步分数占比')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男1000米跑等宽分箱\n",
    "boy_run=pd.cut(boy['男1000米跑分数'],bins = 3)  \n",
    "data=boy_run.value_counts()  # 统计每份数量\n",
    "data\n",
    "\n",
    "plt.figure(figsize=(5,5))  # 设置图片大小\n",
    "_=plt.pie(data,  # 数据\n",
    "          labels=data.index, # 显示标签\n",
    "          textprops={'family':'Kaiti','fontsize':18},  # 设置字体为楷体，字号18号\n",
    "         explode=[0,0,0.15],    # 偏移中⼼量\n",
    "         autopct='%0.2f%%',    # 显示百分⽐\n",
    "         shadow=True)        # 阴影，3D效果\n",
    "\n",
    "plt.title('男生1000米跑步分数占比',fontsize = 18,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "502f48db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男生引体分数占比')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男引体等宽分箱\n",
    "boy_up=pd.cut(boy['男引体分数'],bins = 3)  \n",
    "data=boy_up.value_counts()  # 统计每份数量\n",
    "data\n",
    "\n",
    "plt.figure(figsize=(5,5))  #设置图片大小\n",
    "_=plt.pie(data,  # 数据\n",
    "          labels=data.index,  # 显示标签\n",
    "          textprops={'family':'Kaiti','fontsize':18},\n",
    "         explode=[0,0,0.15],    # 偏移中⼼量\n",
    "         autopct='%0.2f%%',    # 显示百分⽐\n",
    "         shadow=True)        # 阴影，3D效果\n",
    "\n",
    "plt.title('男生引体分数占比',fontsize = 18,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75718fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "478279b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl=pd.read_excel('./体测分数_女生.xls')\n",
    "girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7e362f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75.0, 100.0]    384\n",
       "(50.0, 75.0]     147\n",
       "(-0.1, 25.0]      37\n",
       "(25.0, 50.0]      25\n",
       "Name: 女800米跑分数, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.cut(girl['女800米跑分数'],bins=4)\n",
    "n=x.value_counts()\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "96bd3ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女生800米跑分数直方图')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "x = pd.cut(girl['女800米跑分数'],4)\n",
    "\n",
    "plt.hist(x.value_counts().values,4)\n",
    "\n",
    "plt.title('女生800米跑分数直方图',fontsize = 18,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2d7c73ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Rectangle' object has no property 'list_bin'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24680\\2115746993.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m plt.hist(n,bins=4,\n\u001b[0;32m     13\u001b[0m          \u001b[0mlist_bin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist_bin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m          color='red',density=True)    # 其实就是条形图\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'女生800米跑分数直方图'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfontsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bold'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'white'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbackgroundcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'#c5b783'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[0;32m   2603\u001b[0m         \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2604\u001b[0m         \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacked\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2605\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2606\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2607\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1412\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1413\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1414\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1416\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   6791\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6792\u001b[0m                 \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6793\u001b[1;33m                 \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6794\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlbl\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6795\u001b[0m                     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlbl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m   1065\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mf\"set_{k}\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1066\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1067\u001b[1;33m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[0;32m   1068\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[0;32m   1069\u001b[0m                     \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Rectangle' object has no property 'list_bin'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "\n",
    "x = pd.cut(girl['女800米跑分数'],bins=4)\n",
    "n=x.value_counts()\n",
    "n\n",
    "\n",
    "list_bin=[25,37,147,384]\n",
    "plt.figure(figsize=(5,5))  #设置图片大小\n",
    "plt.rcParams['font.family'] = 'Kaiti'\n",
    "\n",
    "# 绘制直方图\n",
    "plt.hist(n,bins=4,\n",
    "         list_bin=list_bin,\n",
    "         color='red',density=True)    # 其实就是条形图\n",
    "\n",
    "plt.title('女生800米跑分数直方图',fontsize = 18,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "64ba209f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50.0, 75.0]     413\n",
       "(75.0, 100.0]    148\n",
       "(-0.1, 25.0]      19\n",
       "(25.0, 50.0]      13\n",
       "Name: 女跳远分数, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = pd.cut(girl['女跳远分数'],bins=)\n",
    "n1=y1.value_counts()\n",
    "n1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5b675c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女生跳远分数直方图')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "x = pd.cut(girl['女跳远分数'],4)\n",
    "\n",
    "plt.hist(x.value_counts().values,4)\n",
    "\n",
    "plt.title('女生跳远分数直方图',fontsize = 18,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4a90c67a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Rectangle' object has no property 'labels'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24680\\1045832004.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m plt.hist(n1,bins=4,\n\u001b[0;32m     11\u001b[0m          \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m          color='red',density=True)    # 其实就是条形图\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'女生跳远分数直方图'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfontsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bold'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'white'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbackgroundcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'#c5b783'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)\u001b[0m\n\u001b[0;32m   2603\u001b[0m         \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2604\u001b[0m         \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacked\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2605\u001b[1;33m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2606\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2607\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1412\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1413\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1414\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1416\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[0;32m   6791\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6792\u001b[0m                 \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6793\u001b[1;33m                 \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6794\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlbl\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6795\u001b[0m                     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlbl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\hp 14\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m   1065\u001b[0m                     \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mf\"set_{k}\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1066\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1067\u001b[1;33m                         raise AttributeError(f\"{type(self).__name__!r} object \"\n\u001b[0m\u001b[0;32m   1068\u001b[0m                                              f\"has no property {k!r}\")\n\u001b[0;32m   1069\u001b[0m                     \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Rectangle' object has no property 'labels'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "\n",
    "y1 = pd.cut(girl['女跳远分数'],bins=4)\n",
    "n1=y1.value_counts()\n",
    "\n",
    "plt.figure(figsize=(5,5))  #设置图片大小\n",
    "labels=['13','19','148','413']   # 各分数段人数\n",
    "\n",
    "# 绘制直方图\n",
    "plt.hist(n1,bins=4,\n",
    "         labels=labels,\n",
    "         color='red',density=True)    # 其实就是条形图\n",
    "\n",
    "plt.title('女生跳远分数直方图',fontsize = 18,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25) \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66175cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "3、使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖(男女生体重指数参考如下)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "317fa472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男女生体重指数')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = pd.cut(boy['BMI'],bins=[0,16.5,23.3,26.4,100])  # 外圈\n",
    "data1=p1.value_counts()\n",
    "\n",
    "p2 = pd.cut(girl['BMI'],bins=[0,16.5,22.8,25.3,100])  # 内圈\n",
    "data2=p2.value_counts()\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "labels = ['低体重','正常','超重','肥胖']\n",
    "plt.pie(data1,radius=1,\n",
    "        autopct='%0.2f%%', # 半径\n",
    "        pctdistance=0.85,\n",
    "        labels=labels,\n",
    "        wedgeprops={'linewidth':5,  # 间隔的宽度\n",
    "                    'width':0.3,    # 饼图的宽度\n",
    "                    'edgecolor':'White'},# 间隔的颜色\n",
    "        textprops={'family':'Kaiti','fontsize':18})\n",
    "\n",
    "plt.pie(data2,radius=0.7,\n",
    "        autopct='%0.2f%%', # 半径\n",
    "        pctdistance=0.55,\n",
    "        wedgeprops={'linewidth':5,  # 间隔的宽度\n",
    "                    'width':0.7,    # 饼图的宽度\n",
    "                    'edgecolor':'White'},# 间隔的颜色\n",
    "        textprops={'family':'Kaiti','fontsize':18})\n",
    "\n",
    "plt.rcParams['font.family']='Kaiti'  # 全局设置\n",
    "plt.rcParams['font.size']=18\n",
    "plt.legend(['低体重','正常','超重','肥胖'],title='男女生体重指数',prop='Kaiti',)\n",
    "\n",
    "plt.title('男女生体重指数',fontsize = 32,weight='bold', color='white',backgroundcolor='#c5b783',pad = 25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2ae3bdb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
